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*Orthopaedic Surgery > First Aid - Epidemiology and Biostatistics > Flashcards

First Aid - Epidemiology and Biostatistics Flashcards

1
Q

Cross-sectional study

Measures what?

A

Cross-sectional study

Disease prevalence.
Can show risk factor association with disease, but
does not establish causality.

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2
Q

Cross-sectional study

Design of the Study?

A

Cross-sectional study

Frequency of disease and frequency of riskrelated
factors are assessed in the present.
Asks, “What is happening?”

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3
Q

Case-control study

Design of the Study?

A

Case-control study

Compares a group of people with disease to a
group without disease.
Looks to see if odds of prior exposure or risk
factor differ by disease state.
Asks, “What happened?”

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4
Q

Case-control study

Measures what?

A

Case-control study

Odds ratio (OR).
Patients with COPD had higher odds of a
smoking history than those without COPD.

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5
Q

Cohort study

Measures What?

A

Cohort study

Relative risk (RR).
Smokers had a higher risk of developing COPD
than nonsmokers.
Cohort = relative risk.

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6
Q

Cohort study

Measures What?

A

Cohort study

Relative risk (RR).
Smokers had a higher risk of developing COPD
than nonsmokers.
Cohort = relative risk.

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7
Q

Crossover study

Design of the Study?

A

Crossover study

Compares the effect of a series of ≥2 treatments
on a participant.
Order in which participants receive treatments
is randomized. Washout period occurs
between each treatment.

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8
Q

Crossover study

Advantage of the Study?

A

Crossover study

Allows participants to serve as their own
controls.

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9
Q

Twin concordance study

Measures What?

A

Twin concordance study

Measures heritability and influence of
environmental factors (“nature vs nurture”).
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10
Q

Twin concordance study

Design of the Study?

A

Twin concordance study

Compares the frequency with which both
monozygotic twins vs both dizygotic twins
develop the same disease.

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11
Q

Adoption study

Measures What?

A

Adoption study

Measures heritability and influence of
environmental factors.

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12
Q

Adoption study

Design of Study?

A

Adoption study

Compares siblings raised by biological vs
adoptive parents

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13
Q

Clinical Trials

Experimental study involving humans. Compares therapeutic benefits of ≥2 treatments, or of
treatment and placebo. Study quality improves when study is randomized, controlled, and _________
(ie, neither patient nor doctor knows whether the patient is in the treatment or control
group). Triple-blind refers to the additional blinding of the researchers analyzing the data.
Four phases (“Does the drug SWIM?”).

A

Clinical Trials

Experimental study involving humans. Compares therapeutic benefits of ≥2 treatments, or of
treatment and placebo. Study quality improves when study is randomized, controlled, and doubleblinded
(ie, neither patient nor doctor knows whether the patient is in the treatment or control
group). Triple-blind refers to the additional blinding of the researchers analyzing the data.
Four phases (“Does the drug SWIM?”).

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14
Q

Clinical Trials

Experimental study involving humans. Compares therapeutic benefits of ≥2 treatments, or of
treatment and placebo. Study quality improves when study is randomized, controlled, and doubleblinded. \_\_\_\_\_\_\_\_ refers to the additional blinding of the researchers analyzing the data.
Four phases (“Does the drug SWIM?”).
A

Clinical Trials

Experimental study involving humans. Compares therapeutic benefits of ≥2 treatments, or of
treatment and placebo. Study quality improves when study is randomized, controlled, and doubleblinded. **Triple-blind** refers to the additional blinding of the researchers analyzing the data.
Four phases (“Does the drug SWIM?”).
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15
Q

Clinical Trials

Four phases (“Does the drug ____?”).

A

Clinical Trials

Four phases (“Does the drug SWIM?”).

Safe, Works, Improves, postMarketing safe

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16
Q

Clinical Trials - Phase I

Typical study sample?

A

Clinical Trials - Phase I

Small number of either healthy volunteers or
patients with disease of interest

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17
Q

Clinical Trials - Phase I

PURPOSE?

(SWIM)

A

Clinical Trials - Phase I

“Is it Safe?” Assesses safety, toxicity,
pharmacokinetics, and pharmacodynamics.

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18
Q

Clinical Trials - Phase II

PURPOSE

(SWIM)

A

Clinical Trials - Phase II

“Does it Work?” Assesses treatment efficacy,
optimal dosing, and adverse effects

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19
Q

Clinical Trials - Phase II

Typical study sample?

A

Clinical Trials - Phase II

Moderate number of patients with disease of
interest.

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20
Q

Clinical Trials - Phase III

Typical study sample?

A

Clinical Trials - Phase III

Large number of patients randomly assigned
either to the treatment under investigation or
to the standard of care (or placebo).

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21
Q

Clinical Trials - Phase III

PURPOSE?

(SWIM)

A

Clinical Trials - Phase III

“Is it as good or better?” Compares the new
treatment to the current standard of care (any
Improvement?).

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22
Q

Clinical Trials - Phase IV

PURPOSE?

A

Clinical Trials - Phase IV

“Can it stay?” Detects rare or long-term adverse
effects (eg, black box warnings). Can result in
treatment being withdrawn from Market.

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23
Q

Clinical Trials - Phase IV

Typical Study Sample?

A

Clinical Trials - Phase IV

Postmarketing surveillance of patients after
treatment is approved.

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24
Q

Evaluation of Diagnostic tests

Sensitivity and specificity are ____ properties
of a test

A

Evaluation of Diagnostic tests

Sensitivity and specificity are fixed properties
of a test

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25
Q

Evaluation of Diagnostic tests

PPV and NPV vary depending on
disease prevalence in population being tested.

A

Evaluation of Diagnostic tests

PPV and NPV vary depending on
disease _______ in population being tested.

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26
Q

Evaluation of Diagnostic tests

Positive Predictive Value is the number of ____ Positives Devided by the Number of ____ Positives and the Number of ____ Positives

A

Evaluation of Diagnostic tests

Positive Predictive Value is the number of True Positives Devided by the Number of True Positives and the Number of False Positives

(PPV=TP/(TP+FP)

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27
Q

Evaluation of Diagnostic tests

Positive Predictive Value is the number of ____ Positives Devided by the Number of ____ Positives and the Number of ____ Positives

A

Evaluation of Diagnostic tests

Positive Predictive Value is the number of True Positives Devided by the Number of True Positives and the Number of False Positives

(PPV=TP/(TP+FP)

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28
Q

Evaluation of Diagnostic tests

Negative Predictive Value - Definition

A

Evaluation of Diagnostic tests

Probability that a person with a negative test
result actually does not have the disease.

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29
Q

Evaluation of Diagnostic tests

Positive Predictive Value - Definition

A

Evaluation of Diagnostic tests

Probability that a person with a positive test
result actually has the disease.

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30
Q

Evaluation of Diagnostic tests

Sensitivity (True Positive Rate) is actually number of True Positives devided by the number of True ____ and the Number of ____ Negatives.

A

Evaluation of Diagnostic tests

Sensitivity (True Positive Rate) is actually number of True Positives devided by the number of True positives and the Number of False Negatives.

Sensitivity = TP / (TP + FN)

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31
Q

Evaluation of Diagnostic tests

Specificity (True Negative Rate) is actually number of True Negatives devided by the number of True ____ and the Number of ____ Positives.

A

Evaluation of Diagnostic tests

Specificity (True Negative Rate) is actually number of True Negatives devided by the number of True Negatives and the Number of False Positives.

Specificity = TN / (TN + FP)

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32
Q

Evaluation of Diagnostic tests

High Specificity indicates a Low ____ _____ Rate!

A

Evaluation of Diagnostic tests

High Specificity indicates a Low False Positive Rate!

(Specificity = True Negative Rate)

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33
Q

Evaluation of Diagnostic tests

High Sensitivity indicates a Low ____ _____ Rate!

A

Evaluation of Diagnostic tests

High Sensitivity indicates a Low False Negative Rate!

(Sensitivity = True Positive Rate)

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34
Q

Evaluation of Diagnostic tests

___ varies inversely with prevalence or pretest
probability

A

Evaluation of Diagnostic tests

NPV varies inversely with prevalence or pretest
probability

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35
Q

Evaluation of Diagnostic tests

Prevelance = (TP+FN)/(TP+FP+TN+FN)

A

Evaluation of Diagnostic tests

Prevelance = (TP+FN)/(TP+FP+TN+FN)

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36
Q

Evaluation of Diagnostic tests

___ varies directly with pretest probability
(baseline risk, such as prevalence of disease):
high pretest probability → high ___

A

Evaluation of Diagnostic tests

PPV varies directly with pretest probability

(baseline risk, such as prevalence of disease):

high pretest probability → high PPV

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37
Q

Evaluation of Diagnostic tests

A?

A

Evaluation of Diagnostic tests

A = 100% Sensitivity cutoff value

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38
Q

Evaluation of Diagnostic tests

C?

A

Evaluation of Diagnostic tests

C = 100% specificity cutoff value

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39
Q

Evaluation of Diagnostic tests

B?

A

Evaluation of Diagnostic tests

B = practical compromise cutoff between Specificity and Sensitivity

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40
Q

Evaluation of Diagnostic tests

Lowering the cutoff value: B→A

Will cause FP↑ and FN↓

What will happen to the Sensitivity?

A

Evaluation of Diagnostic tests

Sensitivity Rises!

Sensitivity↑ = TP / (TP + FN↓)

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41
Q

Evaluation of Diagnostic tests

Lowering the cutoff value: B→A

Will cause FP↑ and FN↓

What will happen to the Specificty?

A

Evaluation of Diagnostic tests

Specificty Falls!

Specificty↓ =TN / (TN + FP↑)

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42
Q

Evaluation of Diagnostic tests

Lowering the cutoff value: B→A

Will cause FP↑ and FN↓

What will happen to the NPV?

A

Evaluation of Diagnostic tests

NPV Rises!

NPV↑ = TN / (TN + FN↓)

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43
Q

Evaluation of Diagnostic tests

Lowering the cutoff value: B→A

Will cause FP↑ and FN↓

What will happen to the PPV?

A

Evaluation of Diagnostic tests

PPV Falls!

PPV↓ = TP / (TP + FP↑)

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44
Q

Evaluation of Diagnostic tests

Raising the cutoff value: B→C

Will cause FP↓ and FN↑

What will happen to the Specificity?

A

Evaluation of Diagnostic tests

Specificity Rises!

Specificity = TN / (TN + FP↓)

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45
Q

Evaluation of Diagnostic tests

Raising the cutoff value: B→C

Will cause FP↓ and FN↑

What will happen to the NPV?

A

Evaluation of Diagnostic tests

NPV Falls!

NPV = TN / (TN + FN↑)

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46
Q

Evaluation of Diagnostic tests

Raising the cutoff value: B→C

Will cause FP↓ and FN↑

What will happen to the PPV?

A

Evaluation of Diagnostic tests

PPV Rises!

PPV = TP / (TP + FP↓)

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47
Q

Evaluation of Diagnostic tests

Raising the cutoff value: B→C

Will cause FP↓ and FN↑

What will happen to the Sensitivity?

A

Evaluation of Diagnostic tests

Sensitivity Falls!

Sensitivity= TP / (TP + FN↑)

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48
Q

Likelihood ratio

What is it?

A

Likelihood ratio

Likelihood that a given test result would be
expected in a patient with the target disorder
compared to the likelihood that the same result
would be expected in a patient without the
target disorder.

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49
Q

Likelihood ratio

What does LR+>10 indicates?

A

Likelihood ratio

LR+ > 10 indicates a highly SPECIFIC test

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50
Q

Likelihood ratio

What does LR–< 0.1indicates?

A

Likelihood ratio

LR– < 0.1 indicates a highly SENSITIVE test.

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51
Q

Odds ratio

Typically used in Case-Control studies. Represents the odds of exposure among cases (_/c) vs odds of exposure among controls (_/d).

A

Odds ratio

Typically used in Case-Control studies. Represents the odds of exposure among cases (a/c) vs odds of exposure among controls (b/d).

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52
Q

Odds ratio

If in a case-control study, 20/30 lung cancer patients and 5/25 healthy individuals report smoking, the OR is _; so the lung cancer patients are _ times more likely to have a history of smoking.

A

Odds ratio

If in a case-control study, 20/30 lung cancer patients and 5/25 healthy individuals report smoking, the OR is 8; so the lung cancer patients are 8 times more likely to have a history of smoking.

OR = (20/10)/(5/20) = 8

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53
Q

Relative risk

Used in Cohort studies. Risk of developing disease in the
exposed group divided by risk in the unexposed group.

If RR = 1 than ____ ?

A

Relative risk

RR = 1 → NO association between
exposure and disease.

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54
Q

Relative risk

Used in Cohort studies. Risk of developing disease in the
exposed group divided by risk in the unexposed group.

If RR < 1 than ____ ?

A

Relative risk

RR < 1 → exposure associated with
↓ disease occurrence.

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55
Q

Relative risk

Used in Cohort studies. Risk of developing disease in the
exposed group divided by risk in the unexposed group.

If RR < 1 than ____ ?

A

Relative risk

RR < 1 → exposure associated with
↓ disease occurrence.

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56
Q

Relative risk

For rare diseases (low prevalence), __
approximates RR.

A

Relative risk

For rare diseases (low prevalence), OR
approximates RR.

OR = (a/c)/(b/d)

RR = (a/(a+b)/(c/(c+d)

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57
Q

Relative risk

If 5/10 people exposed to radiation are diagnosed with cancer, and 1/10 people not exposed to radiation are diagnosed with cancer, the RR is _ ; so people exposed to radiation have a _ times greater risk of developing cancer.

A

Relative risk

If 5/10 people exposed to radiation are diagnosed with cancer, and 1/10 people not exposed to radiation are diagnosed with cancer, the RR is 5; so people exposed to radiation have a 5 times greater risk of developing cancer.

RR = (5/10)/(1/10) = 5

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58
Q

Relative Risk Reduction

The proportion of risk reduction
attributable to the ________ as
compared to a control.

RRR = 1 - RR

A

Relative Risk Reduction

The proportion of risk reduction
attributable to the intervention as
compared to a control.

RRR = 1 - RR

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59
Q

Relative Risk Reduction

If 2% of patients who receive a flu
shot develop the flu, while 8% of
unvaccinated patients develop the flu,
then RR = _ , and RRR = _ .

RRR = 1 - RR

A

Relative Risk Reduction

If 2% of patients who receive a flu
shot develop the flu, while 8% of
unvaccinated patients develop the flu,
then RR = 2/8 = 0.25, and RRR = 0.75.

RRR = 1 - RR

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60
Q

Attributable Risk

The difference in risk between
________ and ________ groups.

A

Attributable Risk

The difference in risk between
exposed and unexposed groups.

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61
Q

Attributable Risk

If risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then the attributable risk is ___.

A

Attributable Risk

If risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then the attributable risk is 20%

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62
Q

Absolute Risk Reduction

The ______ in risk (not the proportion) attributable to the intervention as compared to a control.

A

Absolute Risk Reduction

The difference in risk (not the proportion) attributable to the intervention as compared to a control.

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63
Q

Absolute Risk Reduction

If 8% of people who receive a placebo vaccine develop the flu vs 2% of people who receive a flu vaccine, then ARR = _.

A

Absolute Risk Reduction

If 8% of people who receive a placebo vaccine develop the flu vs 2% of people who receive a flu vaccine, then ARR = 8%–2% = 6% = 0.06.

64
Q

Number Needed to Treat

Lower number = _____ treatment.

NNT = 1/ARR

A

Number Needed to Treat

Lower number = better treatment.

NNT = 1/ARR

65
Q

Number Needed to Treat

Number of patients who need to be treated for ___.

NNT = 1/ARR

A

Number Needed to Treat

Number of patients who need to be treated for 1 patient to benefit.

NNT = 1/ARR

66
Q

Number Needed to Harm

Number of patients who need to be exposed to a risk factor for ___

NNH = 1/AR

A

Number Needed to Harm

Number of patients who need to be exposed to a risk factor for 1 patient to be harmed.

NNH = 1/AR

67
Q

Number Needed to Harm

Higher number = ___ exposure.

NNH = 1/AR

A

Number Needed to Harm

Higher number = Safer exposure.

NNH = 1/AR

68
Q

Case Fatality Rate

Percentage of deaths occurring among ___.

A

Case Fatality Rate

Percentage of deaths occurring among those with disease.

CFR% = (Deaths/Cases)x100%

69
Q

Case Fatality Rate

If 4 patients die among 10 cases of
meningitis, case fatality rate is ___.

A

Case Fatality Rate

If 4 patients die among 10 cases of
meningitis, case fatality rate is 40%.

CFR% = (Deaths/Cases)x100%

70
Q

Prevelance Vs. Incidence

What is the Difference?

A

Prevelance Vs. Incidence

Incidence looks at new cases (incidents) while Prevalence looks at all current cases.

71
Q

Prevelance Vs. Incidence

Incidence = # of new cases / # of people ___

(per unit of time)

A

Prevelance Vs. Incidence

Incidence = # of new cases / # of people at risk

(per unit of time)

72
Q

Prevelance Vs. Incidence

Prevalence = # of ___ cases / Total # of people

(at a point in time)

A

Prevelance Vs. Incidence

Prevalence = # of existing cases / Total # of people

(at a point in time)

73
Q

Prevelance Vs. Incidence

Prevalence/ (1-Prevelance) = ___ x Average Duration of the Disease

A

Prevelance Vs. Incidence

Prevalence/ (1-Prevelance) = Incidence rate x Average Duration of the Disease

74
Q

Prevelance Vs. Incidence

Prevalence ≈ ____ for short duration disease
(eg, common cold).

A

Prevelance Vs. Incidence

Prevalence ≈ Incidence for short duration disease
(eg, common cold).

75
Q

Prevelance Vs. Incidence

___ > incidence for chronic diseases, due to
large # of existing cases (eg, diabetes).

A

Prevelance Vs. Incidence

Prevalence > incidence for chronic diseases, due to
large # of existing cases (eg, diabetes).

76
Q

Prevelance Vs. Incidence

Prevalence ∼ ______ probability.

A

Prevelance Vs. Incidence

Prevalence ∼ pretest probability.

77
Q

Prevelance Vs. Incidence

↑ ____= ↑ PPV and ↓ NPV.

A

Prevelance Vs. Incidence

↑ Prevalence = ↑ PPV and ↓ NPV.

78
Q

Prevelance Vs. Incidence

Survival Rate ↑ → Prevalence ↑/↓

A

Prevelance Vs. Incidence

Survival Rate↑ → Prevalence↑

79
Q

Prevelance Vs. Incidence

Mortality ↑ → Prevalence ↑/↓

A

Prevelance Vs. Incidence

Mortality↑ → Prevalence↓

80
Q

Prevelance Vs. Incidence

Recovery Time ↑ → Prevalence ↑/↓

A

Prevelance Vs. Incidence

Recovery Time↑ → Prevalence↓

81
Q

Prevelance Vs. Incidence

Extensive Vaccine Administration ↑ → Prevalence ↑/↓

A

Prevelance Vs. Incidence

Extensive Vaccine Administration ↑→ Prevalence↓

82
Q

Prevelance Vs. Incidence

Extensive Vaccine Administration ↑ → Incidence ↑/↓

A

Prevelance Vs. Incidence

Extensive Vaccine Administration ↑→ Incidence↓

83
Q

Prevelance Vs. Incidence

Risk Factor ↓ → Incidence ↑/↓

A

Prevelance Vs. Incidence

Risk Factor ↓ → Incidence↓

84
Q

Prevelance Vs. Incidence

Risk Factor ↓ → Prevelance ↑/↓

A

Prevelance Vs. Incidence

Risk Factor ↓ → Prevelance ↓

85
Q

Accuracy Vs. Precision

The consistency and reproducibility of a test.
The absence of random variation in a test.

True for?

A

Accuracy Vs. Precision

Precision (Relability)

86
Q

Accuracy Vs. Precision

Test Precision (Relability)↑ → Random Error↓/↑

A

Accuracy Vs. Precision

Test Precision (Relability)↑ → Random Error↓

87
Q

Accuracy Vs. Precision

Test Precision (Relability)↑ → Standard Deviation↓/↑

A

Accuracy Vs. Precision

Test Precision (Relability)↑ → Standard Deviation↓

88
Q

Accuracy Vs. Precision

Test Precision (Relability)↑ → Statistical Power (1 − β)↓/↑

A

Accuracy Vs. Precision

Test Precision (Relability)↑ → Statistical Power (1 − β)↑

89
Q

Accuracy Vs. Precision

The closeness of test results to the true values.
The absence of systematic error or bias in a test.

True for?

A

Accuracy Vs. Precision

Accuracy (Validity)

90
Q

Accuracy Vs. Precision

Test Accuracy (Validity)↑→ Systemic Error↓/↑

A

Accuracy Vs. Precision

Test Accuracy (Validity)↑→ Systemic Error↓

91
Q

Receiving Operating Characteristic Curve

ROC curve demonstrates how well a diagnostic
test can ________ between 2 groups (eg,
disease vs healthy).

A

Receiving Operating Characteristic Curve

ROC curve demonstrates how well a diagnostic
test can distinguish between 2 groups (eg,
disease vs healthy).

92
Q

Receiving Operating Characteristic Curve

Plots the ____-positive rate (sensitivity) against the ____-positive rate (1 – specificity).

A

Receiving Operating Characteristic Curve

Plots the True-positive rate (sensitivity) against the False-positive rate (1 – specificity).

93
Q

Receiving Operating Characteristic Curve

The better performing test will have a higher ___, with the curve closer to the upper left corner.

A

Receiving Operating Characteristic Curve

The better performing test will have a higher AUC (area under the curve), with the curve closer to the upper left corner.

94
Q

Bias and Study Errors - Recruiting Participants

Selection Bias - Nonrandom sampling or treatment allocation of subjects such that study population is not representative of _____ population.
Most commonly a sampling bias.

A

Bias and Study Errors - Recruiting Participants

Selection bias - Nonrandom sampling or treatment allocation of subjects such that study population is not representative of target population.
Most commonly a sampling bias.

95
Q

Bias and Study Errors - Recruiting Participants

Selection Bias Example : cases and/
or controls selected from hospitals are less healthy and
have different exposures than general population.

This true for ______ bias

A

Bias and Study Errors - Recruiting Participants

Selection Bias Example : cases and/
or controls selected from hospitals are less healthy and
have different exposures than general population.

This true for Berkson bias

96
Q

Bias and Study Errors - Recruiting Participants

Selection Bias Example : Participants lost to follow up have a different prognosis than those who complete the study.

This true for ______ bias

A

Bias and Study Errors - Recruiting Participants

Selection Bias Example : Participants lost to follow up have a different prognosis than those who complete the study.

This true for Attrition bias

97
Q

Bias and Study Errors - Recruiting Participants

Selection Bias Reduction is done by : __________ and
Ensuring the choice of the right comparison/reference group

A

Bias and Study Errors - Recruiting Participants

Selection Bias Reduction is done by : Randomization and
Ensuring the choice of the right comparison/reference group

98
Q

Bias and Study Errors - Performing study

_____ bias is when Awareness of disorder alters
recall by subjects; common in retrospective studies.

A

Bias and Study Errors - Performing study

Recall bias is when Awareness of disorder alters
recall by subjects; common in retrospective studies.

99
Q

Bias and Study Errors - Performing study

_____ bias is when Awareness of disorder alters
recall by subjects; common in retrospective studies. For example Patients with disease recall exposure after learning of similar cases.

A

Bias and Study Errors - Performing study

Recall bias is when Awareness of disorder alters
recall by subjects; common in retrospective studies. For example Patients with disease recall exposure after learning of similar cases.

100
Q

Bias and Study Errors - Performing study

Recall bias Reduction is done by ___ time from exposure to follow-up

A

Bias and Study Errors - Performing study

Recall bias Reduction is done by Decreasing time from exposure to follow-up

101
Q

Bias and Study Errors - Performing study

________ effect—participants change behavior upon awareness of being observed. This is an exampele for Measurement Bias.

A

Bias and Study Errors - Performing study

Hawthorne effect—participants change behavior upon awareness of being observed. This is an exampele for Measurement Bias.

102
Q

Bias and Study Errors - Performing study

________ effect—participants change behavior upon awareness of being observed. This is an exampele for Measurement Bias.

A

Bias and Study Errors - Performing study

Hawthorne effect—participants change behavior upon awareness of being observed. This is an exampele for Measurement Bias.

103
Q

Bias and Study Errors - Performing study

Reduction of Measurement bias is done by Using objective, ______, and previously tested methods of data collection that are planned ahead of time and Use of _____group

A

Bias and Study Errors - Performing study

Reduction of Measurement bias is done by Using objective, standardized, and previously tested methods of data collection that are planned ahead of time and Use of placebo group

104
Q

Bias and Study Errors - Performing study

Procedure bias is when _______ in different groups are
not treated the same. For Example Patients in treatment group spend more time in highly specialized hospital units

A

Bias and Study Errors - Performing study

Procedure bias is when Subjects in different groups are
not treated the same. For Example Patients in treatment group spend more time in highly specialized hospital units

105
Q

Bias and Study Errors - Performing study

Reuction of Procedure bias is done by _______(masking) and use of ______ reduce influence of participants and
researchers on procedures and interpretation of outcomes as neither are aware of group aassignments

A

Bias and Study Errors - Performing study

Reuction of Procedure bias is done by Blinding (masking) and use of placebo reduce influence of participants and
researchers on procedures and interpretation of outcomes as neither are aware of group aassignments

106
Q

Bias and Study Errors - Performing study

Reuction of Observer-expectancy
bias is done by _______(masking) and use of ______ reduce influence of participants and
researchers on procedures and interpretation of outcomes as neither are aware of group aassignments

A

Bias and Study Errors - Performing study

Reuction of Observer-expectancy
bias is done by Blinding (masking) and use of placebo reduce influence of participants and
researchers on procedures and interpretation of outcomes as neither are aware of group aassignments

107
Q

Bias and Study Errors - Performing study

_____________ bias is when Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (aka, Pygmalion effect). For Example An observer expecting treatment group to show signs
of recovery is more likely to document _______ outcomes.

A

Bias and Study Errors - Performing study

Observer-expectancy bias is when Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (aka, Pygmalion effect). For Example An observer expecting treatment group to show signs
of recovery is more likely to document positive outcomes.

108
Q

Bias and Study Errors - Interpreting results

_______ bias is when Factor related to both exposure and outcome (but not on causal path) distorts effect of exposure on outcome (vs effect modification, in which the exposure leads to different outcomes in subgroups stratified by the factor)

A

Bias and Study Errors - Interpreting results

Confounding bias is when Factor related to both exposure and outcome (but not on causal path) distorts effect of exposure on outcome (vs effect modification, in which the exposure leads to different outcomes in subgroups stratified by the factor)

109
Q

Bias and Study Errors - Interpreting results

An uncontrolled study shows an association between
drinking coffee and lung cancer. However, coffee
drinkers also smoke more, which can account for the
association - this is an example for __________ bias.

A

Bias and Study Errors - Interpreting results

An uncontrolled study shows an association between
drinking coffee and lung cancer. However, coffee
drinkers also smoke more, which can account for the
association - this is an example for Confounding bias.

110
Q

Bias and Study Errors - Interpreting results

Multiple/repeated studies, Crossover studies (subjects act as their own controls), Matching (patients with
similar characteristics in both treatment and control groups)these are reduction methods for __________ bias.

A

Bias and Study Errors - Interpreting results

Multiple/repeated studies, Crossover studies (subjects act as their own controls), Matching (patients with
similar characteristics in both treatment and control groups)these are reduction methods for Confounding bias.

111
Q

Bias and Study Errors - Interpreting results

____-time bias is when Early detection is confused
with elevated survival.

A

Bias and Study Errors - Interpreting results

Lead-time bias is when Early detection is confused
with elevated survival.

112
Q

Bias and Study Errors - Interpreting results

Early detection makes it seem like survival has increased,
but the disease’s natural history has not changed - this is an example for ________

A

Bias and Study Errors - Interpreting results

Early detection makes it seem like survival has increased,
but the disease’s natural history has not changed - this is an example for Lead-time bias

113
Q

Bias and Study Errors - Interpreting results

Early detection makes it seem like survival has increased,
but the disease’s natural history has not changed - this is an example for ________

A

Bias and Study Errors - Interpreting results

Early detection makes it seem like survival has increased,
but the disease’s natural history has not changed - this is an example for Lead-time bias

114
Q

Bias and Study Errors - Interpreting results

Measure “back-end” survival (adjust survival according to the severity of disease at the time of diagnosis) this is an example for Reduction of a Bias - ________

A

Bias and Study Errors - Interpreting results

Measure “back-end” survival (adjust survival according to the severity of disease at the time of diagnosis) this is an example for Reduction of a Bias - Lead-time bias

115
Q

Bias and Study Errors - Interpreting results

_________ bias is reduced by a randomized controlled trial assigning subjects to the screening program or to no
screening.

A

Bias and Study Errors - Interpreting results

  • *Length-time bias** is reduced by a randomized controlled trial assigning subjects to the screening program or to **no
    screening. **
116
Q

Bias and Study Errors - Interpreting results

_____________ is when Screening test detects diseases with long latency period, while those with shorter latency period become symptomatic earlier. For Example a slowly progressive cancer is more likely detected by a screening test than a rapidly progressive cancer.

A

Bias and Study Errors - Interpreting results

Length-time bias is when Screening test detects diseases with long latency period, while those with shorter latency period become symptomatic earlier. For Example a slowly progressive cancer is more likely detected by a screening test than a rapidly progressive cancer.

117
Q

Measures of central tendency

____ = (sum of values)/(total number of values). Most affected by outliers (extreme values).

A

Measures of central tendency

Mean = (sum of values)/(total number of values). Most affected by outliers (extreme values).

118
Q

Measures of central tendency

_____ = middle value of a list of data sorted
from least to greatest. If there is an even number of values, the median will be the average of the middle two values.

A

Measures of central tendency

Median = middle value of a list of data sorted
from least to greatest. If there is an even number of values, the median will be the average of the middle two values.

119
Q

Measures of central tendency

_____ = most common value. Least affected by outliers.

A

Measures of central tendency

Mode = most common value. Least affected by outliers.

120
Q

Measures of Dispersion

Standard ______ = how much variability exists in a set of values, around the mean of these values.

A

Measures of Dispersion

Standard Deviation = how much variability exists in a set of values, around the mean of these values.

121
Q

Measures of Dispersion

Standard ____ = an estimate of how much variability exists in a (theoretical) set of sample means around the true population mean.

A

Measures of Dispersion

Standard Error = an estimate of how much variability exists in a (theoretical) set of sample means around the true population mean.

122
Q

Statistical distribution

Normal Distribution is a Gaussian Disturbtion where
Mean = ___ = ___.

( also called bell-shaped)

A

Statistical distribution

  • *Normal** Distribution is a Gaussian Disturbtion where
  • *Mean = Median = Mode.**

( also called bell-shaped)

123
Q

Non-Normal distributions

______ Distibution Suggests two different populations (eg, metabolic polymorphism such as fast vs
slow acetylators; age at onset of Hodgkin
lymphoma; suicide rate by age).

A

Non-Normal distributions

Bimodal Distibution Suggests two different populations (eg, metabolic polymorphism such as fast vs
slow acetylators; age at onset of Hodgkin
lymphoma; suicide rate by age).

124
Q

Non-Normal distributions

______ skew distribution is where:

Typically, mean > median > mode.

Asymmetry with longer tail on right.

A

Non-Normal distributions

Positive skew distribution is where:

Typically, mean > median > mode.

Asymmetry with longer tail on right.

125
Q

Non-Normal distributions

______ skew distribution is where:

Typically, mean < median < mode.
Asymmetry with longer tail on left

A

Non-Normal distributions

Negative skew distribution is where:

Typically, mean < median < mode.
Asymmetry with longer tail on left

126
Q

Statistical hypotheses

_____ is a Hypothesis of no difference or relationship (eg, there is no association between the disease and the
risk factor in the population).

A

Statistical hypotheses

Null (H0) is a Hypothesis of no difference or relationship (eg, there is no association between the disease and the
risk factor in the population).

127
Q

Statistical hypotheses

_________ is Hypothesis of some difference or relationship (eg, there is some association between the disease and the risk factor in the population).

A

Statistical hypotheses

Alternative Hypothesis (H1) is Hypothesis of some difference or relationship (eg, there is some association between the disease and the risk factor in the population).

128
Q

Outcomes of statistical hypothesis testing

______ result can State that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis).

A

Outcomes of statistical hypothesis testing

Correct result can State that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis).

129
Q

Outcomes of statistical hypothesis testing

______ result can State that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis).

A

Outcomes of statistical hypothesis testing

Correct result can State that there is an effect or difference when one exists (null hypothesis rejected in favor of alternative hypothesis).

130
Q

Incorrect result

Type _ error (_) - Stating that there is an effect or difference when none exists (null hypothesis incorrectly
rejected in favor of alternative hypothesis).

A

Incorrect result

Type I error (α) - Stating that there is an effect or difference when none exists (null hypothesis incorrectly
rejected in favor of alternative hypothesis).

131
Q

Incorrect result

_ is the probability of making a type I error. p is
judged against a preset _ level of significance
(usually set as 0.05). If p < 0.05 for a study outcome,
the probability of obtaining that result purely
by chance is < 5%.

A

Incorrect result

α is the probability of making a type I error. p is
judged against a preset α level of significance
(usually set as 0.05). If p < 0.05 for a study outcome,
the probability of obtaining that result purely
by chance is < 5%.

132
Q

Incorrect result

Type _ Error - Also called false-positive error.
α = you accused an innocent man.(You can never “prove” the alternate hypothesis, but you can reject the null hypothesis as being very unlikely)

A

Incorrect result

  • *Type I Error** - Also called false-positive error.
  • *α** = you accused an innocent man.(You can never “prove” the alternate hypothesis, but you can reject the null hypothesis as being very unlikely)
133
Q

Incorrect result

Type _ error (_) - Stating that there is not an effect or difference when one exists (null hypothesis is not rejected when it is in fact false).

A

Incorrect result

Type II error (β) - Stating that there is not an effect or difference when one exists (null hypothesis is not rejected when it is in fact false).

134
Q

Incorrect result

_ is the probability of making a type II error. _ is related to statistical power (1 – _), which is the probability of rejecting the null hypothesis when it is false.

A

Incorrect result

β is the probability of making a type II error. β is related to statistical power (1 – β), which is the probability of rejecting the null hypothesis when it is false.

135
Q

Incorrect result

Type II Error is Also called ____-negative error.
β = you blindly let the guilty man go free. If you ↑ sample size, you ↑ power. There is power in numbers.

A

Incorrect result

Type II Error is Also called False-negative error.
β = you blindly let the guilty man go free. If you ↑ sample size, you ↑ power. There is power in numbers.

136
Q

Statistical Power

Equals to (1 – β), to ↑ Power and ↓ β:

1) Sample size → ↓/↑
2) Expected Effect size → ↓/↑
3) Precision of Measurement → ↓/↑

A

Statistical Power

Equals to (1 – β), to ↑ Power and ↓ β:

  • *1) Sample size ↑
    2) Expected Effect size ↑
    3) Precision of Measurement ↑**
137
Q

Confidence interval

CI is a Range of values within which the true _____
of the population is expected to fall, with a
_______ probability.

A

Confidence interval

CI is a Range of values within which the true Mean
of the population is expected to fall, with a
Specified probability.

138
Q

Confidence interval

CI for sample mean = x¯ ± Z(SE)
The 95% CI (corresponding to α = .05) is often
used. As sample size increases, CI _______.

A

Confidence interval

CI for sample mean = x¯ ± Z(SE)
The 95% CI (corresponding to α = .05) is often
used. As sample size increases, CI Narrows.

139
Q

Confidence interval

CI for sample mean = x¯ ± Z(SE)
For the 95% CI, Z = ____.
For the 99% CI, Z = ____.

(SE = SD/√n)

A

Confidence interval

CI for sample mean = x¯ ± Z(SE)
For the 95% CI, Z = 1.96.
For the 99% CI, Z = 2.58.

(SE = SD/√n)

140
Q

Confidence interval

If the 95% CI for a mean difference between 2
variables includes _, then there is no significant
difference and H0 is ____.

A

Confidence interval

If the 95% CI for a mean difference between 2
variables includes 0, then there is no significant
difference and H0 is not rejected.

141
Q

Confidence interval

If the 95% CI for odds ratio or relative risk
includes _, H0 is _____.

A

Confidence interval

If the 95% CI for odds ratio or relative risk
includes 1, H0 is not rejected.

142
Q

Confidence interval

If the CIs between 2 groups do not overlap → statistically significant difference _____.

A

Confidence interval

If the CIs between 2 groups do not overlap → statistically significant difference exists.

143
Q

Confidence interval

If the CIs between 2 groups overlap → usually significant difference ______.

A

Confidence interval

If the CIs between 2 groups overlap → usually significant difference doesn’t exists.

144
Q

Meta-Analysis

A method of statistical analysis that pools summary data (eg, means, RRs) from multiple studies for a more precise estimate of the ____ of an effect. Also estimates ________ of effect sizes between studies.

A

Meta-Analysis

A method of statistical analysis that pools summary data (eg, means, RRs) from multiple studies for a more precise estimate of the size of an effect. Also estimates heterogeneity of effect sizes between studies.

145
Q

Meta-Analysis

Improves power, strength of evidence, and ___________of study findings. Limited by quality of
individual studies and ____ in study selection.

A

Meta-Analysis

Improves power, strength of evidence, and generalizability of study findings. Limited by quality of
individual studies and bias in study selection.

146
Q

Common Statistical Tests

_____ - Checks differences between means of 2 groups. (Tea is meant for 2). Example: comparing the mean blood pressure between men and women.

A

Common Statistical Tests

t-test - Checks differences between means of 2 groups. (Tea is meant for 2). Example: comparing the mean blood pressure between men and women.

147
Q

Common Statistical Tests

_____ - Checks differences between means of 3 or more groups. (3 words: ANalysis Of VAriance.)
Example: comparing the mean blood pressure
between members of 3 different ethnic groups.

A

Common Statistical Tests

ANOVA - Checks differences between means of 3 or more groups. (3 words: ANalysis Of VAriance.)
Example: comparing the mean blood pressure
between members of 3 different ethnic groups.

148
Q

Common Statistical Tests

__________ - Checks differences between 2 or more
percentages or proportions of categorical outcomes (not mean values). Pronounce Chi-tegorical. Example: comparing the percentage of members of 3 different ethnic groups who have essential hypertension.

A

Common Statistical Tests

Chi-square (χ²) Checks differences between 2 or more
percentages or proportions of categorical outcomes (not mean values). (Pronounce Chi-tegorical). Example: comparing the percentage of members of 3 different ethnic groups who have essential hypertension.

149
Q

Common Statistical Tests

____________- Checks differences between 2 percentages or proportions of categorical, nominal outcomes. Use instead of chi-square test with small
populations. Example: comparing the percentage of 20 menand 20 women with hypertension.

A

Common Statistical Tests

  • *Fisher’s exact test** - Checks differences between 2 percentages or proportions of categorical, nominal outcomes. Use instead of chi-square test with small
    populations. Example: comparing the percentage of 20 menand 20 women with hypertension.
150
Q

Pearson correlation Coefficient

r is always between __ and __.

A

Pearson correlation Coefficient

r is always between −1 and +1.

151
Q

Pearson correlation Coefficient

The closer the absolute value of r is to _, the stronger the linear correlation between the 2 variables.

A

Pearson correlation Coefficient

The closer the absolute value of r is to 1, the stronger the linear correlation between the 2 variables.

152
Q

Pearson correlation Coefficient

_______ is how much the measured values differ from the average value of r in a data set.

A

Pearson correlation Coefficient

Variance is how much the measured values differ from the average value of r in a data set.

153
Q

Pearson correlation Coefficient

______ r value → _____correlation (as one variable ↑, the other variable ↑).

A

Pearson correlation Coefficient

Positive r value → positive correlation (as one variable ↑, the other variable ↑).

154
Q

Pearson correlation Coefficient

______ r value → _____correlation (as one variable ↓, the other variable ↑).

A

Pearson correlation Coefficient

Negative r value → negative correlation (as one variable ↓, the other variable ↑).

155
Q

Pearson correlation Coefficient

Coefficient of determination = __ (amount of variance in one variable that can be explained by variance in another variable).

A

Pearson correlation Coefficient

Coefficient of determination = r2 (amount of variance in one variable that can be explained by variance in another variable).

*Orthopaedic Surgery (28 decks)

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